Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Computer Vision
Code: 45518Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Digital Humanities and Heritage | OP | 1 |
Contact lecturer
- Name :
- Juan Antonio Barceló Álvarez
- Email :
- juanantonio.barcelo@uab.cat
Teaching staff
- Oriol Ramos Terrades
- Sònia Boadas Cabarrocas
Teaching staff (external to UAB)
- David R. Gonzàlez
- Hector Orengo
Group languages
You can consult this information at the end of the document.
Prerequisites
No prior knowledge of computer science or programming is required, except for familiarity with computer equipment at an advanced user level. The required level of mathematics is that of compulsory secondary education.
Some familiarity with humanities and/or cultural topics is recommended.
English proficiency sufficient to read texts is required
It is assumed that subject 45527 Digital Cultural heritage has already been passed.
Objectives
This course covers the use of computer vision technologies and digital video in cultural and humanistic fields. It introduces methods for digital image processing, semantic annotation, cataloging, and indexing. Regarding 3D modeling, it focuses on object recognition through artificial intelligence techniques and builds upon what was introduced in course 45527 Digital Cultural Heritage, delving more deeply into geometric models, reconstruction (digital anastylosis), rendering, and animation.
Learning outcomes
- CA16 (Explain the operation of computer vision systems that provide concrete solutions to problems arising from public use and open access to culture.) Explain the operation of computer vision systems that provide concrete solutions to problems arising from public use and open access to culture.
- CA17 (Describe the limits and drawbacks of some of the computer vision methodologies applied to the study and dissemination of historical and cultural heritage.) Describe the limits and drawbacks of some of the computer vision methodologies applied to the study and dissemination of historical and cultural heritage.
- KA19 (Identify the different computer vision technologies that can be used in cultural and humanistic studies.) Identify the different computer vision technologies that can be used in cultural and humanistic studies.
- KA20 (Identify different ways of managing the geometric information of a visual model by adding semantic information.) Identify different ways of managing the geometric information of a visual model by adding semantic information.
- SA23 (Edit geometric models resulting from 3D scanning of historical and architectural objects.) Edit geometric models resulting from 3D scanning of historical and architectural objects.
- SA24 (Render geometric models resulting from 3D scanning of historical and architectural objects.) Render geometric models resulting from 3D scanning of historical and architectural objects.
- SA25 (Use different technologies and approaches in the virtual reconstruction of heritage elements.) Use different technologies and approaches in the virtual reconstruction of heritage elements.
Contents
- Introduction to Digital Photography and Digital Image Processing
- Advanced Methods for the Analysis and Processing of Historical Images and Ancient Documents. Cataloging and Annotation. Segmentation.
- Use of Multispectral Images for the Analysis and Restoration of Historical Documents and Works of Art
- Image Recognition and Classification. Introduction to Machine Learning
- Image Recognition and Classification. Image Preprocessing
- Image Recognition and Classification. Convolutional Neural Networks in Art History and Other Cultural Studies
- Image Recognition and Classification. Convolutional Neural Networks in Archaeology
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Personal Study and Bibliographical consultation | 60 | 2.4 | CA16, CA17, KA19, KA20, SA23, SA24, SA25 |
| Suplementary practical work with computer equipment | 34 | 1.36 | CA17, KA19, KA20, SA23, SA24, SA25 |
| Practical work at lab with computer equipment | 18 | 0.72 | SA23, SA24, SA25 |
| attendance at lectures led by the professor | 18 | 0.72 | CA16, CA17, KA19, KA20 |
Attendance at theoretical classes led by the professor.
Attendance at seminar sessions and practicals using computers and specific software, directed by the professor.
Classes are held in a specialized computer lab.
Comprehensive reading of texts.
The student is expected to make an independent effort to consult specialized bibliography. Part of the documentation is in English.
Class debates, moderated by the teaching staff, on the most significant topics.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Presenting a wriiten and critical text using Generative Artificial Intelligence | 30% | 5 | 0.2 | CA16, CA17, KA19, KA20, SA23, SA24, SA25 |
| Presentation of written comments based on suggested bibliography | 30% | 5 | 0.2 | CA16, CA17, KA19, KA20, SA23, SA24, SA25 |
| Evaluation of practical work suggested by the professor | 40% | 10 | 0.4 | CA17, KA19, KA20, SA23, SA24, SA25 |
The assessment methodology for this master's program is based on the active and reflective participation of students. Their analytical skills will be evaluated through practical exercises using computer software as assigned by the teaching staff. In addition, students will be asked to provide commentary on articles and bibliographic references.
At the end of the course, students must prepare critical summaries of different technologies, expressing and arguing for best practice criteria.
Another key component of the assessment will be a critical assignment involving the use of Generative Artificial Intelligence tools, applied to one of the topics covered during the course. This task must include a reflection on the limitations and potential of these technologies within the field of Digital Humanities. Specific details regarding format, criteria, and deadlines will be explained and discussed in class by the teaching staff.
Single final assessment is not permitted.
When each assessment activity is assigned, the teaching staff will inform students (via Moodle) of the procedure and the date for grade review.
Resit procedure: only the final task (critical summary) will be eligible for resubmission. This decision will be made on a case-by-case basis following a personal interview between the student and the teaching staff.
The resubmission deadline will also be determined on a case-by-case basis and by mutual agreement between the teaching staff and the student.
A student will receive a grade of \"Not assessable\" if none of the required assessment activities are submitted.
If a student commits any irregularity that could significantly affect the grade of an assessment activity, that activity will be graded with a 0, regardless of any disciplinary proceedings that may be initiated. If multiple irregularities are detected in the assessment activities of the same course, the final grade will be 0.
This course encourages the use of Artificial Intelligence (AI) technologies as an integral part of task development, provided that the final outcome reflects a significant contribution from the student in terms of analysis and personal reflection. The student must:
(i) identify which parts were generated using AI;
(ii) specify the tools used; and
(iii) include a critical reflection on how these tools influenced the process and the final outcome of the activity.
Lack of transparency in the use of AI in this assessed activity will be considered academic dishonesty and will be penalized with a grade of 0 with no possibility of resubmission, or with more severe sanctions in the most serious cases.
Bibliography
Archana, R., & Jeevaraj, P. E. (2024). Deep learning models for digital image processing: a review. Artificial intelligence review, 57(1), 11.
Bouman, C. A. (2022). Foundations of computational imaging: a model-based approach. Society for Industrial and Applied Mathematics.
Bovik, A. C. (2010). Handbook of image and video processing. Academic press.
Burger, W., & Burge, M. J. (2022). Digital image processing: An algorithmic introduction. Springer Nature.
Chen, B., & Chen, S. (2026). Image processing. In Machine Vision Technology (pp. 17-31). Singapore: Springer Nature Singapore.
Chen, C. H. (Ed.). (2015). Handbook of pattern recognition and computer vision. World scientific.
Chowdhary, C. L., Reddy, G. T., & Parameshachari, B. D. (2022). Computer vision and recognition systems: research innovations and trends. Apple Academic Press.
Ghai, D., Tripathi, S. L., Saxena, S., Chanda, M., & Alazab, M. (Eds.). (2022). Machine learning algorithms for signal and image processing. John Wiley & Sons.
Li, C., Li, X., Chen, M., & Sun, X. (2023, July). Deep learning and image recognition. In 2023 IEEE 6th international conference on electronic information and communication technology (ICEICT) (pp. 557-562). IEEE.
Nixon, M., & Aguado, A. S. (2025). Feature extraction and image processing for computer vision. Academic press.
Scherer, R. (2020). Computer vision methods for fast image classification and retrieval. Springer International Publishing.
Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature.
Toennies, K. D. (2024). An introduction to image classification. springer nature singapore Pte Ltd.
Zhang, X. (2022). Application of artificial intelligence recognition technology in digital image processing. Wireless Communications and Mobile Computing, 2022(1), 7442639.
Software
In general:
The GIMP, https://www.gimp.org/
Google Teachable Machines, https://teachablemachine.withgoogle.com/
Other software will be recommended thorugh the course
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (SEMm) Seminars (master) | 1 | Spanish | second semester | afternoon |